In the previous post I showed how to solve a simple problem by performing an Analysis of Variance (if you haven’t read it, please click here). The example was about three different methods to perform a task and we wanted to know if we are getting different results or the variations on the data are only due to the population dispersion.
Generally speaking, we compared the variances between the three samples and the variances within each sample and used the Variance Ratio Test to know, with a specific confidence interval, if the samples are coming from the same population, which in practical terms (referred to our example) means that the different methods are not changing the outcomes. In our example we rejected this hypothesis, so we can say that there is a significant difference between the methods. Continue reading
In manufacturing environments, as well as in many other settings, we take actions and modify parameters, procedures and processes to obtain a specific result (usually to improve the situation). In these cases we need to know if the obtained result is a expected consequence of our changes or we’re just observing variations inherent to the population, that are not related to our actions.
All people involved in maintenance activities know that some equipment are really easy to maintain while others can make maintenance work a real nightmare. This attribute is referred as maintainability and I’m going to discuss it and relate it to maintenance, reliability and availability concepts.
The thing is that all equipment is running without stopping; but breakdowns and production stops are not the only factor that affects productivity. Think about a machine that produces something, and due to some problem it only works at 90% of its capacity. The machine is working continuously, but we are still losing 10% of production. In fact, in a 10-hour shift, losses are equivalent to the machine being stopped for one hour!